English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To maximize clinical benefit, clinicians routinely tailor treatment to theindividual characteristics of each patient, where individualized treatmentrules are needed and are of significant research interest to statisticians. Inthe covariate-adjusted randomization clinical trial with many covariates, wemodel the treatment effect with an unspecified function of a single index ofthe covariates and leave the baseline response completely arbitrary. We devisea class of estimators to consistently estimate the treatment effect functionand its associated index while bypassing the estimation of the baselineresponse, which is subject to the curse of dimensionality. We further developinference tools to identify predictive covariates and isolate effectivetreatment region. The usefulness of the methods is demonstrated in bothsimulations and a clinical data example.

Flexible Inference of Optimal Individualized Treatment Strategy in Covariate Adjusted Randomization with Multiple Covariates